Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods
Characterizing exoplanetary atmospheres via Bayesian retrievals requires assuming some chemistry model, such as thermochemical equilibrium or parameterized abundances. The higher-resolution data offered by upcoming telescopes enable more complex chemistry models within retrieval frameworks. Yet many...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Planetary Science Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/PSJ/acc939 |
| _version_ | 1797328105308684288 |
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| author | Michael D. Himes Joseph Harrington Atılım Güneş Baydin |
| author_facet | Michael D. Himes Joseph Harrington Atılım Güneş Baydin |
| author_sort | Michael D. Himes |
| collection | DOAJ |
| description | Characterizing exoplanetary atmospheres via Bayesian retrievals requires assuming some chemistry model, such as thermochemical equilibrium or parameterized abundances. The higher-resolution data offered by upcoming telescopes enable more complex chemistry models within retrieval frameworks. Yet many chemistry codes that model more complex processes like photochemistry and vertical transport are computationally expensive, and directly incorporating them into a 1D retrieval model can result in prohibitively long execution times. Additionally, phase-curve observations with upcoming telescopes motivate 2D and 3D retrieval models, further exacerbating the lengthy runtime for retrieval frameworks with complex chemistry models. Here we compare thermochemical equilibrium approximation methods based on their speed and accuracy with respect to a Gibbs energy-minimization code. We find that, while all methods offer orders-of-magnitude reductions in computational cost, neural network surrogate models perform more accurately than the other approaches considered, achieving a median absolute dex error of <0.03 for the phase space considered. While our results are based on a 1D chemistry model, our study suggests that higher-dimensional chemistry models could be incorporated into retrieval models via this surrogate modeling approach. |
| first_indexed | 2024-03-08T06:47:10Z |
| format | Article |
| id | doaj.art-6f71bdc1a7f44aefa931e76cf3e357fc |
| institution | Directory Open Access Journal |
| issn | 2632-3338 |
| language | English |
| last_indexed | 2024-03-08T06:47:10Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Planetary Science Journal |
| spelling | doaj.art-6f71bdc1a7f44aefa931e76cf3e357fc2024-02-03T07:36:51ZengIOP PublishingThe Planetary Science Journal2632-33382023-01-01447410.3847/PSJ/acc939Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation MethodsMichael D. Himes0https://orcid.org/0000-0002-9338-8600Joseph Harrington1https://orcid.org/0000-0002-8955-8531Atılım Güneş Baydin2https://orcid.org/0000-0001-9854-8100Planetary Sciences Group, Department of Physics, University of Central Florida , USA ; mhimes@knights.ucf.eduPlanetary Sciences Group, Department of Physics and Florida Space Institute, University of Central Florida , USADepartment of Computer Science, University of Oxford , UKCharacterizing exoplanetary atmospheres via Bayesian retrievals requires assuming some chemistry model, such as thermochemical equilibrium or parameterized abundances. The higher-resolution data offered by upcoming telescopes enable more complex chemistry models within retrieval frameworks. Yet many chemistry codes that model more complex processes like photochemistry and vertical transport are computationally expensive, and directly incorporating them into a 1D retrieval model can result in prohibitively long execution times. Additionally, phase-curve observations with upcoming telescopes motivate 2D and 3D retrieval models, further exacerbating the lengthy runtime for retrieval frameworks with complex chemistry models. Here we compare thermochemical equilibrium approximation methods based on their speed and accuracy with respect to a Gibbs energy-minimization code. We find that, while all methods offer orders-of-magnitude reductions in computational cost, neural network surrogate models perform more accurately than the other approaches considered, achieving a median absolute dex error of <0.03 for the phase space considered. While our results are based on a 1D chemistry model, our study suggests that higher-dimensional chemistry models could be incorporated into retrieval models via this surrogate modeling approach.https://doi.org/10.3847/PSJ/acc939Exoplanet atmospheric compositionExoplanet atmospheresNeural networksRegressionChemical abundances |
| spellingShingle | Michael D. Himes Joseph Harrington Atılım Güneş Baydin Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods The Planetary Science Journal Exoplanet atmospheric composition Exoplanet atmospheres Neural networks Regression Chemical abundances |
| title | Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods |
| title_full | Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods |
| title_fullStr | Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods |
| title_full_unstemmed | Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods |
| title_short | Toward 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods |
| title_sort | toward 3d retrieval of exoplanet atmospheres assessing thermochemical equilibrium estimation methods |
| topic | Exoplanet atmospheric composition Exoplanet atmospheres Neural networks Regression Chemical abundances |
| url | https://doi.org/10.3847/PSJ/acc939 |
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